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I did deeplearning. The projects are not too overwhelming but each project gets a key thing done, so they are super useful. I keep on finding myself getting back and looking at the source code from your projects, much more than I do from other courses. Deep Learning Tutorials. How in the world do you gather enough images when training deep learning models? And to make matters worse, manually annotating an image dataset can be a time consuming, tedious, and even expensive process.
So is there a way to leverage the power of Google Images to quickly gather training images and thereby cut down on the time it takes to build your dataset? Updated April 20, The JavaScript in this post has been updated because the previous method was no-longer working.
Please refer to the updated code below. As a kid Christmas time was my favorite time of the year — and even as an adult I always find myself happier when December rolls around.
Looking back on my childhood, my dad always went out well of his way to ensure Christmas was a magical time. I thank Michael for the original inspiration of this blog post. The first step in using Google Images to gather training data for our Convolutional Neural Network is to head to Google Images and enter a query. The next step is to use a tiny bit of JavaScript to gather the image URLs which we can then download using Python later in this tutorial.
From there, click the Console tab:. The next step is to start scrolling! From there, we manually intervene with JavaScript. This function effectively simulates right clicking on an image shown in your browser. Notice how the click involves dispatching both a mousedown and mouseup event followed by activating the context menu.
Each of our URLs will be in the contents parameter passed to our createDownload function. Here we first create a hiddenElement. We then populate it with the contents , create a destination link with a filename of urls. Ultimately when the createDownload function runs, your browser will trigger a download. Depending on your browser settings, your download may go to your default download location or you may be prompted to select a name and location for your image URLs file download.
Our grabUrls function creates what JavaScript calls a Promise. Our final snippet which you need to paste into the Javascript console is what calls our grabUrls function:. Our main entry point to start execution is this call to grabUrls.
As you can see, the createDownload function is called from here as the final step. While this method calls our functions we defined in the JavaScript console directly, alternatively, you could use the logic to create a Chrome Browser plugin without too much hassle. This is left as an exercise for you to complete. If you are having trouble following this guide, please see the video at the very top of this blog post where I provide step-by-step instructions.
And as another reminder, the code shown above is an update to this blog post. The YouTube video at the top of this post will not and does not match this updated code, but the manual JavaScript console intervention is the same. Simply paste each snippet into the console until you have your URLs text file. Now that we have our urls. Using Python and the requests library , this is quite easy. Here we are just importing required packages.
Notice requests on Line 4 — this will be the package we use for downloading the image content. Command line argument parsing is handled on Lines — we only require two:.
From there, we load each URL from the file into a list on Line Using requests , we just need to specify the url and a timeout for the download. We attempt to download the image file into a variable, r , which holds the binary file along with HTTP headers, etc. We then create a file pointer, f , specifying our path, p , and indicating that we want write mode in binary format "wb" on Line Subsequently, we write our files contents r. This is covered in our last code block:.
Common reasons for an image being unable to load include an error during the download such as a file not downloading completely , a corrupt image, or an image file format that OpenCV cannot read.
Lastly if the delete flag was set, we call os. As you can see, example images from Google Images are being downloaded to my machine as training data.
The error you see in the output is normal — you should expect these. You should also expect some images to be corrupt and unable to open — these images get deleted from our dataset. My favorite way to do this is to use the default tools on my macOS machine.
After pruning my downloaded images I have a total of images as training to our Not Santa app. I have put together a step-by-step video that demonstrates me performing the above steps to gather deep learning training data using Google Images.
Note: Keep in mind that this video was created in to accompany the original article; however, this blog post has now been updated in April So how do I get rid of all these duplicate images? Will they affect my deep learning model at training time? There will be duplicate images in your dataset using the Google Images method.
And if you leave them in for your training exercise, your model may form a bias towards a particular image it sees multiple times. Therefore, head on over to my tutorial on how to Detect and remove duplicate images from a dataset for deep learning. There, you will learn what image hashing is and how it will help you to automatically remove duplicates with a simple Python script.
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Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV.
I created this website to show you what I believe is the best possible way to get your start. Sweet post Adrian! However, it costs you a small amount of money and you need an Azure account.
Selenium is also good for tricks like that. And one more thing. Selenium can automatically find tags than urls on google image searcher and download big list of photos.
Selenium is fantastic for stuff like this, I totally agree. Using the tags is a great way to expand the search as well. If other readers want to try this I would suggest that you manually look at the tags to ensure the images are relevant before doing this. You can of course prune them out later but one of the goals here is to reduce the human intervention. The case here is to make as little and as fast as possible. I like to make it quick and automatic.
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